BrainSight: Auto-Encoder based Brain Tumor Classification System Using Convolutional Neural Networks

Abstract

"Brain tumor detection from Magnetic Resonance Imaging (MRI) scans is a critical aspect of clinical diagnosis and treatment planning in neuro-oncology. Manual interpretation of MRI images by radiologists is time-consuming and prone to human error. This thesis addresses the challenge by proposing an innovative approach that combines autoencoder-based dimensionality reduction with ensemble Convolutional Neural Networks (CNNs) for improved brain tumor detection. The autoencoder extracts essential features from high-dimensional MRI images while reducing dimensionality, enhancing computational efficiency, and aiding subsequent classification. Simultaneously, the ensemble CNN architecture aggregates the predictive power of multiple models, mitigating overfitting risks and enhancing overall robustness. This research used an autoencoder to condense MRI images, then combined multiple CNN models in an ensemble for better classification. Testing on diverse datasets showed higher accuracy and efficiency than current methods. This system promises effective brain tumor detection, aiding clinical workflows and enhancing patient outcomes in neuro-oncology. The brain tumor detection system developed in this project achieved compelling performance metrics on MRI images. With an accuracy of 90.6%, precision of 0.93, recall of 0.876, F1 score of 0.903, and an AUC of 0.91, the system demonstrates robustness and effectiveness in identifying tumors. By leveraging autoencoder-based dimensionality reduction and an ensemble of CNN models, the approach not only enhances diagnostic accuracy but also streamlines clinical workflows. These results underscore the system's potential as a valuable tool for early tumor detection, treatment planning, and disease monitoring in neuro-oncology, contributing to improved patient care outcomes. "